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  <title>Description of acfModify</title>
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  <meta name="description" content="Modify aggregate channel features object detector.">
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<h1>acfModify
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<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>Modify aggregate channel features object detector.</strong></div>

<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function detector = acfModify( detector, varargin ) </strong></div>

<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> Modify aggregate channel features object detector.

 Takes an object detector trained by acfTrain() and modifies it. Only
 certain modifications are allowed to the detector and the detector should
 never be modified directly (this may cause the detector to be invalid and
 cause segmentation faults). Any valid modification to a detector after it
 is trained should be performed using acfModify().

 The parameters 'nPerOct', 'nOctUp', 'nApprox', 'lambdas', 'pad', 'minDs'
 modify the channel feature pyramid created (see help of chnsPyramid.m for
 more details) and primarily control the scales used. The parameters
 'pNms', 'stride', 'cascThr' and 'cascCal' modify the detector behavior
 (see help of acfTrain.m for more details). Finally, 'rescale' can be
 used to rescale the trained detector (this change is irreversible).

 USAGE
  detector = acfModify( detector, pModify )

 INPUTS
  detector   - detector trained via acfTrain
  pModify    - parameters (struct or name/value pairs)
   .nPerOct    - [] number of scales per octave
   .nOctUp     - [] number of upsampled octaves to compute
   .nApprox    - [] number of approx. scales to use
   .lambdas    - [] coefficients for power law scaling (see BMVC10)
   .pad        - [] amount to pad channels (along T/B and L/R)
   .minDs      - [] minimum image size for channel computation
   .pNms       - [] params for non-maximal suppression (see bbNms.m)
   .stride     - [] spatial stride between detection windows
   .cascThr    - [] constant cascade threshold (affects speed/accuracy)
   .cascCal    - [] cascade calibration (affects speed/accuracy)
   .rescale    - [] rescale entire detector by given ratio

 OUTPUTS
  detector   - modified object detector

 EXAMPLE

 See also chnsPyramid, <a href="bbNms.html" class="code" title="function bbs = bbNms( bbs, varargin )">bbNms</a>, <a href="acfTrain.html" class="code" title="function detector = acfTrain( varargin )">acfTrain</a>, <a href="acfDetect.html" class="code" title="function bbs = acfDetect( I, detector, fileName )">acfDetect</a>

 Piotr's Computer Vision Matlab Toolbox      Version 3.20
 Copyright 2014 Piotr Dollar.  [pdollar-at-gmail.com]
 Licensed under the Simplified BSD License [see external/bsd.txt]</pre></div>





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